Research article Special Issues

Performance evaluation of brain state discrimination using near-infrared spectroscopy for brain-computer interface: an exploratory case study

  • Received: 31 December 2023 Revised: 27 May 2024 Accepted: 03 June 2024 Published: 06 June 2024
  • A new method of environmental control that does not depend on motor functions is eagerly awaited to support independent living for people with severe quadriplegia. In this study, we conducted an exploratory case study of brain state discrimination in a quadriplegic subject to develop a brain-computer interface controlled by a mental task execution. We measured near-infrared spectroscopy (NIRS) signals in a patient with a cervical spinal cord injury while performing mental tasks. A block design with a task and a rest separated by 30 seconds was used to measure brain function. The utilized mental tasks were mental arithmetic and Japanese word chains. Seventeen trials of the NIRS signal were acquired for each task, and 52 samples with 24-dimensional features per trial data were extracted. Random forest was used as the classifier, and the number of correct responses in the binary discrimination of the brain states were calculated by cross-validation. The exact binomial test was used for the statistical analysis, and a two-tailed test with a significance level of 5% was performed. The results showed that the number of correct responses was 15 out of 17 (p = 0.002) for the mental arithmetic task and 14 out of 17 (p = 0.013) for the Japanese word chains task, for an overall accuracy of 85%. These results indicate that this method can discriminate the brain state of a patient with quadriplegia from the NIRS signal. By applying these findings to a brain-computer interface, it will be possible to provide a new means of environmental control for individuals with quadriplegia.

    Citation: Akira Masuo, Takuto Sakuma, Shohei Kato. Performance evaluation of brain state discrimination using near-infrared spectroscopy for brain-computer interface: an exploratory case study[J]. AIMS Bioengineering, 2024, 11(2): 173-184. doi: 10.3934/bioeng.2024010

    Related Papers:

  • A new method of environmental control that does not depend on motor functions is eagerly awaited to support independent living for people with severe quadriplegia. In this study, we conducted an exploratory case study of brain state discrimination in a quadriplegic subject to develop a brain-computer interface controlled by a mental task execution. We measured near-infrared spectroscopy (NIRS) signals in a patient with a cervical spinal cord injury while performing mental tasks. A block design with a task and a rest separated by 30 seconds was used to measure brain function. The utilized mental tasks were mental arithmetic and Japanese word chains. Seventeen trials of the NIRS signal were acquired for each task, and 52 samples with 24-dimensional features per trial data were extracted. Random forest was used as the classifier, and the number of correct responses in the binary discrimination of the brain states were calculated by cross-validation. The exact binomial test was used for the statistical analysis, and a two-tailed test with a significance level of 5% was performed. The results showed that the number of correct responses was 15 out of 17 (p = 0.002) for the mental arithmetic task and 14 out of 17 (p = 0.013) for the Japanese word chains task, for an overall accuracy of 85%. These results indicate that this method can discriminate the brain state of a patient with quadriplegia from the NIRS signal. By applying these findings to a brain-computer interface, it will be possible to provide a new means of environmental control for individuals with quadriplegia.



    加载中

    Acknowledgments



    This work was supported in part by Grants-in-Aid for Scientific Research from the Japan Society for the Promotion of Science (KAKENHI grant numbers: JP19H01137, JP20H04018, and 23K20012).

    Use of AI tools declaration



    The authors declare they have not used Artificial Intelligence (AI) tools in the creation of this article.

    Conflict of interest



    The authors declare no conflict of interest associated with this manuscript.

    Author contributions



    Masuo designed and executed the experiments and wrote the manuscript. Sakuma and Kato are supervisors and edited the manuscript. All authors had approved the final version.

    [1] Nas K, Yazmalar L, Şah V, et al. (2015) Rehabilitation of spinal cord injuries. World J Orthop 6: 8-16. https://doi.org/10.5312/wjo.v6.i1.8
    [2] Zbogar D, Eng JJ, Miller WC, et al. (2017) Movement repetitions in physical and occupational therapy during spinal cord injury rehabilitation. Spinal Cord 55: 172-179. https://doi.org/10.1038/sc.2016.129
    [3] Yao DPG, Inoue K, Sy MP, et al. (2020) Experience of Filipinos with spinal cord injury in the use of assistive technology: An occupational justice perspective. Occup Ther Int 1–10. https://doi.org/10.1155/2020/6696296
    [4] Wolpaw JR, Birbaumer N, Heetderks WJ, et al. (2000) Brain-computer interface technology: A review of the first international meeting. IEEE Trans Rehabil Eng 8: 164-173. https://doi.org/10.1109/tre.2000.847807
    [5] Naseer N, Hong KS (2015) fNIRS-based brain-computer interfaces: A review. Front Hum Neurosci 9: 3. https://doi.org/10.3389/fnhum.2015.00003
    [6] Farwell LA, Donchin E (1988) Talking off the top of your head: Toward a mental prosthesis utilizing event-related brain potentials. Electroencephalogr Clin Neurophysiol 70: 510-523. https://doi.org/10.1016/0013-4694(88)90149-6
    [7] Said RR, Heyat MB, Song K, et al. (2022) A systematic review of virtual reality and robot therapy as recent rehabilitation technologies using EEG-brain-computer interface based on movement-related cortical potentials. Biosensors 12: 1134. https://doi.org/10.3390/bios12121134
    [8] Jöbsis FF (1977) Noninvasive, infrared monitoring of cerebral and myocardial oxygen sufficiency and circulatory parameters. Science 198: 1264-1267. https://doi.org/10.1126/science.929199
    [9] Delpy DT, Cope M, van der Zee P, et al. (1988) Estimation of optical pathlength through tissue from direct time of flight measurement. Phys Med Biol 33: 1433-1442. https://doi.org/10.1088/0031-9155/33/12/008
    [10] Mihara M, Miyai I (2016) Review of functional near-infrared spectroscopy in neurorehabilitation. Neurophotonics 3: 031414. https://doi.org/10.1117/1.NPh.3.3.031414
    [11] Hwang HJ, Lim JH, Kim DW, et al. (2014) Evaluation of various mental task combinations for near-infrared spectroscopy-based brain-computer interfaces. J Biomed Opt 19: 77005. https://doi.org/10.1117/1.JBO.19.7.077005
    [12] Naseer N, Hong MJ, Hong KS (2014) Online binary decision decoding using functional near-infrared spectroscopy for the development of brain-computer interface. Exp Brain Res 232: 555-564. https://doi.org/10.1007/s00221-013-3764-1
    [13] Sitaram R, Zhang H, Guan C, et al. (2007) Temporal classification of multichannel near-infrared spectroscopy signals of motor imagery for developing a brain-computer interface. Neuroimage 34: 1416-1427. https://doi.org/10.1016/j.neuroimage.2006.11.005
    [14] Hamid H, Naseer N, Nazeer H, et al. (2022) Analyzing classification performance of fNIRS-BCI for gait rehabilitation using deep neural networks. Sensors 22: 1932. https://doi.org/10.3390/s22051932
    [15] Schreuder M, Riccio A, Risetti M, et al. (2013) User-centered design in brain-computer interfaces-a case study. Artif Intell Med 59: 71-80. https://doi.org/10.1016/j.artmed.2013.07.005
    [16] Kasahara T, Terasaki K, Ogawa Y, et al. (2012) The correlation between motor impairments and event-related desynchronization during motor imagery in ALS patients. BMC Neurosci 13: 66. https://doi.org/10.1186/1471-2202-13-66
    [17] Bradford DS, McBride GG (1987) Surgical management of thoracolumbar spine fractures with incomplete neurologic deficits. Clin Orthop Relat Res 218: 201-216. https://journals.lww.com/clinorthop/abstract/1987/05000
    [18] Jasper H (1958) Report of the committee on methods of clinical examination in electroencephalography: 1957. Electroencephalogr Clin Neurophysiol 10: 370-375. https://doi.org/10.1016/0013-4694(58)90053-1
    [19] Hoshi Y, Kobayashi N, Tamura M (2001) Interpretation of near-infrared spectroscopy signals: A study with a newly developed perfused rat brain model. J Appl Physiol 90: 1657-1662. https://doi.org/10.1152/jappl.2001.90.5.1657
    [20] Masuo A, Sakuma T, Kato S (2023) Discriminating brain activation state in a patient with Duchenne muscular dystrophy using near-infrared spectroscopy for communication: An exploratory case study. Asian J Occup Ther 19: 1-8. https://doi.org/10.11596/asiajot.19.55
    [21] Hidano N, Fukuhara M, Iwawaki S, et al. (2000) Manual for the State-trait Anxiety Inventory-Form JYZ (in Japanese). Tokyo: Jitsumu Kyoiku-Shuppan 23-29.
    [22] Scherer R, Müller-Putz GR, Pfurtscheller G (2009) Flexibility and practicality graz brain-computer interface approach. Int Rev Neurobiol 86: 119-131. https://doi.org/10.1016/S0074-7742(09)86009-1
    [23] Coyle S, Ward T, Markham C, et al. (2004) On the suitability of near-infrared (NIR) systems for next-generation brain-computer interfaces. Physiol Meas 25: 815-822. https://doi.org/10.1088/0967-3334/25/4/003
    [24] Breiman L (2001) Random forests. Mach Learn 45: 5-32. https://doi.org/10.1023/A:1010933404324
    [25] Kursa M, Rudnicki W (2010) Feature selection with the boruta package. J Stat Softw 36: 1-13. https://doi.org/10.18637/jss.v036.i11
    [26] Becht E, McInnes L, Healy J, et al. (2018) Dimensionality reduction for visualizing single-cell data using UMAP. Nat Biotechnol 37: 38-44. https://doi.org/10.1038/nbt.4314
    [27] Müller-Putz G, Scherer R, Brunner C, et al. (2008) Better than random? A closer look on BCI results. Int J Bioelectromagn 10: 52-55. https://www.ijbem.org
    [28] Caliandro P, Molteni F, Simbolotti C, et al. (2020) Exoskeleton-assisted gait in chronic stroke: An EMG and functional near-infrared spectroscopy study of muscle activation patterns and prefrontal cortex activity. Clin Neurophysiol 131: 1775-1781. https://doi.org/10.1016/j.clinph.2020.04.158
    [29] Nakai Y, Nakamura M, Tomida M, et al. (2022) Brain-computer interface using fNIRS waveforms when recalling the experience of eating savory and spicy instant noodle. J Adv Inf Technol 13: 381-386. https://doi.org/10.12720/jait.13.4.381-386
    [30] Weyand S, Schudlo L, Takehara-Nishiuchi K, et al. (2015) Usability and performance-informed selection of personalized mental tasks for an online near-infrared spectroscopy brain-computer interface. Neurophotonics 2: 025001. https://doi.org/10.1117/1.NPh.2.2.025001
  • Reader Comments
  • © 2024 the Author(s), licensee AIMS Press. This is an open access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0)
通讯作者: 陈斌, bchen63@163.com
  • 1. 

    沈阳化工大学材料科学与工程学院 沈阳 110142

  1. 本站搜索
  2. 百度学术搜索
  3. 万方数据库搜索
  4. CNKI搜索

Metrics

Article views(659) PDF downloads(38) Cited by(0)

Article outline

Figures and Tables

Figures(7)  /  Tables(1)

Other Articles By Authors

/

DownLoad:  Full-Size Img  PowerPoint
Return
Return

Catalog